We compare this model to two other methods: Max-pooling and CAE-Siamese.
Max-pooling is a common baseline SSE model.
It consists in max-pooling input frame-level features across time to get a fixed-size vector.
The CAE-Siamese referenced in [1]}, [2]} is on par with the current state-of-the-art in SSE models. We took the implementation from [2]} which is a three-layers LSTM encoder-decoder trained with a weighted average of the Siamese loss as in [4]} and the Correspondance loss from [5]}. Positive pairs for training are sampled using PLP features as in [2]}. We know from [7]} that the Correspondance loss benefits from recent frame-level speech representation. Therefore, we train the CAE-Siamese with Wav2vec2.0 features (Base model at layer 8) to give a general idea of the current best practices in SSE modelling.
